

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin



class 						_A     (				snake_case						,    snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Union[str, Any]					=   1





  @register_to_config
  def __init__(  self   ,SCREAMING_SNAKE_CASE_=2000   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=20   ,SCREAMING_SNAKE_CASE_=1E-3    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		None
      snake_case							:		List[Any]        =		None
      snake_case							:		List[str]        =		None





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		int        =		torch.linspace(1   ,self.config.sampling_eps   ,SCREAMING_SNAKE_CASE_   ,device=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=None    ):

      '''simple docstring'''
      if self.timesteps is None:
          raise ValueError(
              """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      snake_case							:		List[str]        =		(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      snake_case							:		Any        =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
      snake_case							:		Tuple        =		std.flatten()
      while len(std.shape    ) < len(score.shape    ):
          snake_case							:		Tuple        =		std.unsqueeze(-1    )
      snake_case							:		Union[str, Any]        =		-score / std

      # compute
      snake_case							:		List[str]        =		-1.0 / len(self.timesteps    )

      snake_case							:		Tuple        =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      snake_case							:		Any        =		beta_t.flatten()
      while len(beta_t.shape    ) < len(x.shape    ):
          snake_case							:		Optional[Any]        =		beta_t.unsqueeze(-1    )
      snake_case							:		Optional[int]        =		-0.5 * beta_t * x

      snake_case							:		str        =		torch.sqrt(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		drift - diffusion**2 * score
      snake_case							:		int        =		x + drift * dt

      # add noise
      snake_case							:		Tuple        =		randn_tensor(x.shape   ,layout=x.layout   ,generator=SCREAMING_SNAKE_CASE_   ,device=x.device   ,dtype=x.dtype    )
      snake_case							:		str        =		x_mean + diffusion * math.sqrt(-dt    ) * noise

      return x, x_mean





  def __len__(  self    ):

      '''simple docstring'''
      return self.config.num_train_timesteps











'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


